Data Analysis for Internal Auditing

Why Attend

Processes are becoming more complex; and simple audit techniques and random audit samples are not enough anymore to provide factual evidence to draw conclusions. Given this, it is only through proper data analysis and smart sampling that auditors can perform their responsibilities in an effective and efficient way.

This course develops critical skills in analyzing and interpreting data, which are essential in today's data-driven world. The course equips participants with knowledge and techniques to identify patterns, trends, and relationships in data, enabling them to make strategic decisions that benefit their organizations. Additionally, participants gain skills to confidently design and execute effective audit sampling plans that can greatly improve the efficiency and effectiveness of the audit process.

Course Methodology

Through group exercises, case studies and real-time data bundles, participants will turn raw data into knowledge to understand patterns and identify red flags, or deviations from procedures and standards. Moreover, participants will go through several audit sampling techniques that will help them develop representative and efficient sample sizes that can be applied in their day-to-day missions.

Course Objectives

By the end of the course, participants will be able to:

• Demonstrate a comprehensive understanding of data analysis techniques
• Understand the impact of data analysis on audit sampling
• Use statistical tools to analyze information, manipulate data and conduct audit sampling
• Apply different audit sampling methods to different scenarios
• Identify data anomalies and deviations in audit results to simplify the reporting process

Target Audience

This course is suitable for all Internal Auditors, and those involved in the internal auditing process including, but not limited to, internal controllers, risk officers, external auditors and compliance officers from all levels.

Target Competencies

• Data mining
• Mitigating risks
• Data analysis
• Understanding trends
• Searching for anomalies

Introduction to data analysis

• Definition and history
•  Current technology, the growing availability of data, and increasing challenges
• The impact of vast volumes of data
• Understanding when and how to corroborate data
• Rethinking the value and usage of data
• Getting real value from the data

The different sampling methods

• Sampling and non-sampling risks
• Statistical and non-statistical sampling
• Random sample vs. population census
• Sampling method vs. sample size

Benefits and risks of sampling techniques such as:

• Random sampling
• Stratified sampling
• Interval sampling
• Subjective sampling
• Block sampling
• Systematic sampling
• Dollar unit sampling
• Stop or go sampling

Comparison and benchmarking

• Practice Advisory 2320
• Institute of Internal Auditors (IIA) recommendations and publications
• Performance benchmarking
• The evolution of big data
• Evaluating the effectiveness of new data analysis techniques
• Peer benchmarking

Data mining vs. audit sampling

• Different data analysis techniques and selecting the sample
• Employing methods for adjusting sampling size
• Simple excel functions and queries to perform analyses
• Incorporating fraud red flags in the audit sampling
• Practice combining results for different scenarios

Identifying data anomalies and deviations

• Fundamental concepts of data anomalies and deviations
• Importance and impact on business operations
• Techniques for identifying, cleaning, and transforming data
• Types such as outliers, missing values, and errors

 How to identify, analyze and highlight them

• Statistical methods
• Visualization techniques to identify and highlight data anomalies and deviation
• Real-world examples and cases in different industries and business domains